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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It does disclose the key behavior—returning either the next question or a final verdict—but it does not mention whether the flow state is mutated, what happens for invalid choices, or any session/state requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single, information-dense sentence. The title and description work together without repetition or filler, and the key input/output behavior is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Adequate for a two-parameter tool: it names both inputs and describes the possible return. However, with no output schema and no annotations, it falls short of fully describing the response shape, error behavior, or what the caller should do with the returned next question/final verdict.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, so the description's clarification that `question` is a question id and `choice` is the chosen option's index is essential. It adds meaning beyond the raw property names and types, though it omits details like how to obtain valid question ids or bounds beyond minimum 0.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific operation: given a question id and a choice index, return the next question or final verdict. This clearly differentiates it from sibling tools like checker_start or checker_tree, which have different roles in the flow.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied: use this when a user has answered a question and the flow should advance. However, it does not explicitly state when not to use it, what prerequisites exist (e.g., starting with checker_start), or how it relates to alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full behavioral disclosure burden. It reveals that the tool is the first question in a guide, but does not describe whether repeated calls create sessions, whether any state is managed, or what exact output the agent should expect beyond being a question.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, short sentence with no filler words. It front-loads the key information about the tool's position in the decision guide and earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the very low complexity of a zero-parameter starting tool and the lack of annotations or output schema, the one-sentence description is nearly complete. It identifies the tool as the entry point of the guide, though adding a note about return value or follow-up steps would make it fully self-contained.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has zero parameters, so the baseline is 4 and no parameter documentation is needed. The description adds the relevant meaning that this is the starting question, which is sufficient for a parameterless tool.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description identifies the tool as 'the first question' of the Workplace recycling duty checker decision guide, making its role as the entry point clear. It does not use an imperative verb like 'starts', but the title and description together communicate the function and distinguish it from siblings like checker_answer and checker_tree.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'first question' implies this tool should be used at the beginning of the decision guide, giving some contextual guidance. However, it does not explicitly state when not to use it or mention alternatives such as checker_answer or checker_tree as follow-up options.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

There are no annotations, so the description carries the behavioral burden. It discloses that the tool contains the entire decision tree, which signals a read-style data access, but it does not explicitly state that it is read-only or describe how the tree is returned. For a zero-parameter retrieval tool this is adequate but minimal.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that names the artifact and its purpose without filler. Every word earns its place, and it is easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a parameterless tool, the description is mostly complete: it tells the agent what content is covered — questions, options, and verdicts — and why it would be used. It stops short of giving explicit selection criteria relative to the sibling tools, but the low complexity and absence of parameters keep the gap small.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With zero parameters and 100% schema coverage, there is nothing for the description to add about parameter meaning. The baseline of 4 applies because parameter semantics are effectively moot for this tool.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a concrete artifact — every question, option and verdict of the Workplace recycling duty checker guide — so the agent knows what this tool exposes. It lacks an explicit verb such as 'return' or 'get', and the title carries some of the meaning, but it is clearly distinguished from the step-by-step sibling tools by its 'whole decision tree' scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'for reasoning end to end' implies the tool is for whole-tree reasoning rather than single-step answers. It does not explicitly state when to choose this over checker_answer or checker_start, nor does it give any exclusions, so the usage guidance is implied rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden and does this well: it explicitly states that nothing is bought, ordered, or paid, that no quote is guaranteed, that it is free, and what the tool returns. This gives the agent accurate expectations about side effects and limitations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, front-loaded with the most important instruction ('Read first'), and every sentence adds useful information. It avoids redundancy with the schema because there is no schema content to repeat.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given zero parameters and no output schema, the description provides complete context for an agent: what the tool does, what it does not do, and what information it returns. The agent can invoke it correctly without needing additional details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so there is no parameter confusion to resolve. The description adds value by explaining what information the tool returns, which is more relevant than parameter semantics in this case.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as an explanatory/read-first helper that describes what submit_enquiry does, distinguishing it from the actual submission tool and sibling tools. It names the resource (Workplace Recycling Rules enquiry) and the specific outcome: an enquiry with human providers, not a purchase or guaranteed quote.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The opening 'Read first' strongly implies the tool should be used before submit_enquiry, providing clear contextual guidance. It does not explicitly name alternative tools or state when not to use it, but the context is sufficiently clear for an agent to understand its intended place in the workflow.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must carry the behavioral burden. It implies a read-only retrieval of field metadata, but it never explicitly states that the tool performs no mutation, what format the response takes, or whether any authentication or preconditions apply. The content is well specified, but the behavioral traits are only implicit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single focused sentence that front-loads the core purpose and then lists the exact data points returned. The follow-up instruction about submit_enquiry adds practical value without bloat. Every clause earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter, output-only metadata tool, the description is complete: it explains what the tool returns, enumerates the included attributes, and ties the output to the next step in the workflow. The absence of an output schema is compensated by the explicit enumeration of fields.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so there are no parameter semantics to explain. The description adds a useful note that the returned field keys should be used as keys when passing answers to submit_enquiry, which helps the agent understand the role of the output even though it is not a parameter of this tool.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description identifies a specific resource ('the Workplace Recycling Rules enquiry') and enumerates exactly what fields are exposed: key, label, type, required status, help text, and allowed options. This clearly distinguishes it from a submit action like submit_enquiry and from checker tools, since it is the field-metadata listing for the enquiry.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear contextual usage: it tells the agent to pass answers to submit_enquiry keyed by the field key returned here. This effectively connects the tool to the submission workflow. It does not explicitly name alternatives or say when not to use it, but the intended role is evident.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden and does so: it discloses the validate-then-confirm flow, that step 1 returns a summary/consent line/token, that the tool is not a purchase or guaranteed quote, and exactly what consent entails including third-party data sharing and how to stop it. This is unusually rich disclosure for a mutation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core purpose and the negative framing before the procedural detail, and every clause does work. It is long, and the verbatim consent text is repeated from the schema's consent description, which is mild redundancy rather than padding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a three-parameter, nested-object, no-annotations, no-output-schema tool, the description supplies everything an agent needs: the two-step contract, which parameters apply at which step, the approval gate, and the post-submission email confirmation. Nothing material is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning beyond the schema: it explains that `answers` must be keyed by field key from enquiry_fields, that `consent` must be true in both steps, and that `confirmation` is the token from step 1 required only after approval. That sequencing context is not derivable from the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ("Submits an enquiry to Workplace Recycling Rules") and immediately disambiguates it from adjacent concepts ("NOT a purchase, NOT a guaranteed quote"), which distinguishes it from the checker_* and enquiry_* siblings. An agent can tell what this does without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit two-step protocol: step 1 with answers + consent=true, step 2 only after the person agrees, with the confirmation token and the same answers. It also names the gating condition ("only if the person agrees") and the downstream requirement (the person must click an emailed link before providers see it).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.